Analysis and prediction of cancerlectins using evolutionary and domain information
Additional File-1
Table S1:p-values for compositional differences in cancerlectins and non-cancerlectins residues.
Amino acids / Cancerlectins Composition / Non-cancerlectinsComposition / p-value
Alanine (Ala) / 6.855 / 6.418 / 0.15963963
Cysteine (Cys) / 2.758 / 2.686 / 0.72324009
Aspartic Acid (Asp) / 4.669 / 5.466 / 0.00290293
Glutamic Acid (Glu) / 6.125 / 5.86 / 0.46065121
Phenylalanine (Phe) / 3.903 / 4.231 / 0.08462309
Glycine (Gly) / 7.423 / 7.647 / 0.47278063
Histidine (His) / 2.313 / 2.151 / 0.33262026
Isoleucine (Ile) / 4.405 / 4.751 / 0.108295
Lysine (Lys) / 5.151 / 5.468 / 0.00761836
Leucine (Leu) / 9.102 / 8.236 / 0.009655763
Methionine (Met) / 2.071 / 1.873 / 0.15336505
Asparagine (Asn) / 4.509 / 5.303 / 0.00367459
Proline (Pro) / 5.817 / 4.904 / 0.00758714
Glutamine (Gln) / 4.68 / 4.217 / 0.03682272
Arginine (Arg) / 4.865 / 4.209 / 0.00638263
Serine (Ser) / 8.458 / 8.489 / 0.93652765
Threonine (Thr) / 5.981 / 6.355 / 0.19734236
Valine (Val) / 6.043 / 6.347 / 0.05255521
Tryptophan (Trp) / 1.823 / 2.004 / 0.24435425
Tyrosine (Tyr) / 3.05 / 3.382 / 0.04970098
(Bold values show the residues with significant differences in composition of cancer and non-cancerlectins.)
Table S2: Performance of BLAST on individual test sets of cancerlectins at E-value cutoff of 0.001.
Testsets / Total Proteins / Total Hits / No Hits / Correct Hits / % coverage1 / 36 / 28 / 8 / 13 / 36.11
2 / 36 / 31 / 5 / 13 / 36.11
3 / 36 / 25 / 11 / 13 / 36.11
4 / 35 / 27 / 8 / 16 / 45.71
5 / 35 / 32 / 3 / 19 / 54.29
(Note: The five-fold cross validation technique was used for BLAST, in which 4 testsets (consisting of positive and negative proteins) formed the database and the fifth test set was used as a query at E-value cutoff of 0.001.)
Table S3: The performance of SVM model (Learning Parameter: -z c –t 2 –g 0.01 –c 0.5 –j 1) using Amino acid composition method.
Threshold / Sensitivity / Specificity / Accuracy / Std. Error / MCC-1.0 / 94.38 / 16.35 / 50.75 / 1.07861207 / 0.16
-0.9 / 91.59 / 25.19 / 54.46 / 1.68705957 / 0.21
-0.8 / 89.35 / 31.83 / 57.18 / 1.1612123 / 0.25
-0.7 / 87.08 / 36.26 / 58.66 / 0.91615283 / 0.27
-0.6 / 83.14 / 43.35 / 60.89 / 0.94253912 / 0.29
-0.5 / 79.21 / 50.89 / 63.36 / 1.07868624 / 0.31
-0.4 / 75.27 / 57.97 / 65.59 / 0.53710334 / 0.33
-0.3 / 67.97 / 64.15 / 65.84 / 0.93955521 / 0.32
-0.2 / 59.51 / 71.24 / 66.08 / 2.34335571 / 0.31
-0.1 / 52.21 / 74.78 / 64.85 / 1.69668029 / 0.28
0.0 / 44.87 / 77.88 / 63.36 / 2.12551735 / 0.24
0.1 / 38.70 / 80.53 / 62.12 / 1.93892083 / 0.21
0.2 / 33.06 / 82.30 / 60.64 / 1.74465871 / 0.17
0.3 / 29.14 / 83.18 / 59.40 / 1.85411057 / 0.14
0.4 / 23.57 / 86.28 / 58.67 / 2.04288864 / 0.13
0.5 / 17.94 / 88.06 / 57.18 / 1.5617407 / 0.09
0.6 / 15.14 / 91.59 / 57.93 / 2.02443819 / 0.11
0.7 / 9.54 / 93.35 / 56.44 / 1.83891925 / 0.06
0.8 / 6.74 / 94.24 / 55.70 / 1.92436483 / 0.04
0.9 / 5.62 / 96.45 / 56.44 / 1.58794962 / 0.07
1.0 / 3.95 / 97.78 / 56.44 / 1.26829413 / 0.06
(Bold value indicates the point where overall best result was achieved.)
Table S4: The performance of SVM model (Learning Parameter: -z c –t 2 –g 0.001 –c 5 –j 1) using Dipeptide composition method.
Threshold / Sensitivity / Specificity / Accuracy / Std. Error / MCC-1.0 / 94.98 / 12.37 / 48.77 / 1.38234077 / 0.13
-0.9 / 93.86 / 15.91 / 50.25 / 1.06547923 / 0.15
-0.8 / 91.60 / 25.17 / 54.46 / 2.17020183 / 0.21
-0.7 / 87.08 / 32.25 / 56.44 / 1.48087001 / 0.22
-0.6 / 82.56 / 41.13 / 59.40 / 0.99075527 / 0.26
-0.5 / 79.75 / 46.01 / 60.88 / 1.15143649 / 0.27
-0.4 / 75.21 / 53.12 / 62.86 / 1.90852194 / 0.29
-0.3 / 67.27 / 62.84 / 64.84 / 2.98782128 / 0.30
-0.2 / 58.84 / 69.03 / 64.60 / 2.62439136 / 0.28
-0.1 / 52.67 / 74.33 / 64.85 / 1.74050567 / 0.28
0.0 / 47.07 / 78.76 / 64.85 / 2.84033625 / 0.28
0.1 / 37.49 / 81.85 / 62.37 / 4.62120158 / 0.21
0.2 / 28.00 / 86.28 / 60.65 / 5.13250621 / 0.18
0.3 / 21.83 / 88.50 / 9.17 / 6.44662284 / 0.14
0.4 / 16.78 / 89.84 / 57.68 / 7.81502847 / 0.10
0.5 / 11.21 / 91.17 / 55.95 / 8.83469943 / 0.05
0.6 / 8.41 / 92.95 / 55.70 / 8.72782527 / 0.03
0.7 / 6.16 / 94.28 / 55.45 / 9.20294877 / 0.03
0.8 / 5.59 / 96.47 / 56.44 / 8.82459234 / 0.06
0.9 / 4.46 / 97.36 / 56.44 / 8.87582526 / 0.04
1.0 / 3.91 / 97.36 / 56.19 / 8.90266398 / 0.03
(Bold value indicates the point where overall best result was achieved.)
Table S5: The performance of SVM model (Learning Parameter: -z c –t 2 –g 0.001 –c 1 –j 1) using Split amino acid composition (2-part) method.
Threshold / Sensitivity / Specificity / Accuracy / Std. Error / MCC-1.0 / 93.27 / 22.57 / 53.72 / 1.33145034 / 0.21
-0.9 / 88.20 / 27.00 / 53.96 / 1.5810914 / 0.19
-0.8 / 87.09 / 32.75 / 56.68 / 1.65861569 / 0.23
-0.7 / 85.40 / 40.26 / 60.14 / 1.09102979 / 0.28
-0.6 / 83.72 / 46.03 / 62.62 / 0.9581837 / 0.32
-0.5 / 77.51 / 53.99 / 64.35 / 0.53852948 / 0.32
-0.4 / 73.05 / 59.32 / 65.35 / 0.71950678 / 0.33
-0.3 / 66.32 / 64.18 / 65.10 / 0.68139122 / 0.31
-0.2 / 55.60 / 68.59 / 62.87 / 1.00936317 / 0.24
-0.1 / 47.73 / 74.77 / 62.87 / 1.61213213 / 0.23
0.0 / 39.86 / 77.00 / 60.65 / 2.1069205 / 0.18
0.1 / 37.64 / 80.10 / 61.40 / 2.31116291 / 0.20
0.2 / 31.44 / 84.07 / 60.89 / 2.47200647 / 0.18
0.3 / 26.94 / 87.18 / 60.65 / 2.51265398 / 0.18
0.4 / 23.59 / 88.50 / 59.91 / 2.38838942 / 0.17
0.5 / 21.32 / 89.39 / 59.41 / 2.15194935 / 0.16
0.6 / 18.49 / 91.16 / 59.16 / 1.57553356 / 0.15
0.7 / 15.16 / 92.49 / 58.43 / 1.36975034 / 0.14
0.8 / 10.65 / 95.14 / 57.93 / 1.32726787 / 0.12
0.9 / 7.86 / 96.02 / 57.19 / 1.18798737 / 0.09
1.0 / 6.17 / 96.91 / 56.94 / 0.81293911 / 0.07
(Bold value indicates the point where overall best result was achieved.)
Table S6: The performance of SVM model (Learning Parameter: -z c –t 2 –g 0.0001 –c 1 –j 1) using Split amino acid composition (4-part) method.
Threshold / Sensitivity / Specificity / Accuracy / Std. Error / MCC-1 / 94.92 / 18.15 / 51.98 / 0.88282728 / 0.2
-0.9 / 91.56 / 25.21 / 54.46 / 1.0410024 / 0.22
-0.8 / 85.92 / 36.27 / 58.16 / 0.780496 / 0.25
-0.7 / 80.89 / 50.46 / 63.86 / 1.39494444 / 0.33
-0.6 / 77.5 / 59.78 / 67.58 / 2.03182529 / 0.38
-0.5 / 65.12 / 66.85 / 66.09 / 1.86624114 / 0.32
-0.4 / 52.78 / 71.27 / 63.11 / 1.50855693 / 0.25
-0.3 / 44.92 / 77.44 / 63.12 / 1.48927029 / 0.24
-0.2 / 33.13 / 83.63 / 61.39 / 1.37081217 / 0.2
-0.1 / 24.68 / 86.72 / 59.41 / 1.68996154 / 0.16
0 / 22.46 / 89.83 / 60.16 / 1.12411565 / 0.18
0.1 / 17.41 / 92.05 / 59.17 / 1.45766114 / 0.16
0.2 / 12.94 / 95.59 / 59.16 / 1.05396205 / 0.17
0.3 / 9.01 / 95.59 / 57.43 / 0.78845418 / 0.1
0.4 / 6.76 / 96.46 / 56.93 / 1.04180324 / 0.08
0.5 / 3.92 / 97.35 / 56.19 / 0.27544509 / 0.03
0.6 / 1.11 / 97.8 / 55.2 / 0.38147608 / -0.04
0.7 / 0 / 98.24 / 54.96 / 0.56082618 / -0.06
0.8 / 0 / 98.24 / 54.96 / 0.56082618 / -0.06
0.9 / 0 / 99.12 / 55.45 / 0.312506 / -0.04
1 / 0 / 99.12 / 55.45 / 0.312506 / -0.04
(Bold value indicates the point where overall best result was achieved)
Table S7: The performance of SVM model (Learning Parameter: -z c –t 2 –g 7 –c 1 –j 1) using PSSM-based method.
Threshold / Sensitivity / Specificity / Accuracy / Std Error / MCC-1.0 / 92.73 / 17.3 / 50.51 / 2.31580353 / 0.14
-0.9 / 91.06 / 22.61 / 52.76 / 3.83473076 / 0.17
-0.8 / 89.92 / 30.55 / 56.72 / 3.81677351 / 0.24
-0.7 / 87.65 / 35.86 / 58.7 / 3.87443983 / 0.26
-0.6 / 85.41 / 40.29 / 60.19 / 4.35370027 / 0.27
-0.5 / 82.62 / 49.13 / 63.9 / 4.80963575 / 0.33
-0.4 / 77.54 / 56.19 / 65.63 / 3.98193169 / 0.34
-0.3 / 71.89 / 60.14 / 65.38 / 4.08809173 / 0.32
-0.2 / 67.92 / 68.57 / 68.34 / 2.53125186 / 0.36
-0.1 / 56.68 / 72.99 / 65.86 / 2.31250384 / 0.3
0.0 / 46.6 / 77.86 / 64.11 / 2.07999663 / 0.26
0.1 / 42.65 / 80.52 / 63.87 / 1.73345147 / 0.25
0.2 / 37.03 / 83.19 / 62.87 / 2.60860001 / 0.23
0.3 / 31.43 / 84.95 / 61.39 / 2.86866066 / 0.2
0.4 / 26.95 / 86.73 / 60.4 / 2.1362013 / 0.18
0.5 / 17.99 / 90.73 / 58.67 / 1.46569233 / 0.13
0.6 / 14.6 / 92.49 / 58.17 / 1.04287775 / 0.12
0.7 / 12.37 / 94.26 / 58.17 / 1.01415186 / 0.13
0.8 / 6.17 / 96.02 / 56.44 / 0.54705941 / 0.06
0.9 / 4.51 / 97.35 / 56.44 / 0.67124064 / 0.06
1.0 / 3.38 / 98.68 / 56.69 / 0.80362553 / 0.07
(Bold value indicates the point where overall best result was achieved)
Table S8: The performance of SVM model (Learning Parameter: -z c –t 2 –g 7 –c 5 –j 1) using PSSM-PROSITE domain based method.
Threshold / Sensitivity / Specificity / Accuracy / Std Error / MCC-1 / 93.33 / 15.53 / 49.78 / 3.16984069 / 0.13
-0.9 / 91.63 / 20.41 / 51.76 / 2.86094635 / 0.16
-0.8 / 89.38 / 29.74 / 55.98 / 4.50216326 / 0.22
-0.7 / 86.57 / 36.81 / 58.71 / 4.60636039 / 0.26
-0.6 / 83.75 / 41.23 / 59.94 / 4.70744262 / 0.27
-0.5 / 82.64 / 48.27 / 63.41 / 4.23941812 / 0.32
-0.4 / 80.35 / 54.44 / 65.87 / 2.93361211 / 0.36
-0.3 / 77.54 / 59.75 / 67.6 / 2.66434495 / 0.37
-0.2 / 73.6 / 65.93 / 69.34 / 3.18494176 / 0.39
-0.1 / 68 / 69.9 / 69.09 / 3.45016724 / 0.38
0 / 62.97 / 74.32 / 69.34 / 3.48271676 / 0.38
0.1 / 60.16 / 77.86 / 70.08 / 3.52700581 / 0.39
0.2 / 55.67 / 78.75 / 68.59 / 3.50745834 / 0.35
0.3 / 52.3 / 82.31 / 69.09 / 3.68722172 / 0.37
0.4 / 44.41 / 83.64 / 66.36 / 3.48105587 / 0.31
0.5 / 37.08 / 87.16 / 65.11 / 1.96940346 / 0.28
0.6 / 28.11 / 89.82 / 62.63 / 1.7620227 / 0.23
0.7 / 21.37 / 91.15 / 60.4 / 1.1029116 / 0.18
0.8 / 17.44 / 93.8 / 60.15 / 1.07899212 / 0.18
0.9 / 11.25 / 95.57 / 58.42 / 1.25914812 / 0.13
1 / 9.02 / 96.46 / 57.92 / 1.07996018 / 0.12
(Bold value indicates the point where overall best result was achieved.)
Table S9: All reported PROSITE domains in cancer and non-cancerlectins.
No. of PROSITE Domains / Cancerlectins-178 / No. of PROSITE domains / Non-cancerlectin-2262 / PS01180 / 2 / PS01180
1 / PS50004 / 1 / PS50022
1 / PS50017 / 1 / PS50024
1 / PS50020 / 1 / PS50038
1 / PS50025 / 1 / PS50082
1 / PS50027 / 1 / PS50157
1 / PS50050 / 1 / PS50176
1 / PS50093 / 1 / PS50215
1 / PS50095 / 1 / PS50279
1 / PS50097 / 1 / PS50290
1 / PS50214 / 1 / PS50294
1 / PS50215 / 1 / PS50800
1 / PS50222 / 1 / PS50820
1 / PS50227 / 1 / PS50847
1 / PS50228 / 1 / PS50900
1 / PS50237 / 1 / PS51046
1 / PS50240 / 1 / PS51073
1 / PS50261 / 1 / PS51111
1 / PS50268 / 1 / PS51174
1 / PS51111 / 1 / PS51189
1 / PS51132 / 1 / PS51190
1 / PS51212 / 1 / PS51211
1 / PS51257 / 1 / PS51233
2 / PS50024 / 1 / PS51257
2 / PS50049 / 2 / PS50025
2 / PS50217 / 2 / PS50027
2 / PS50221 / 2 / PS50068
2 / PS50287 / 2 / PS50092
2 / PS50941 / 2 / PS50234
2 / PS51092 / 2 / PS50268
4 / PS50963 / 2 / PS50853
7 / PS50915 / 2 / PS50941
7 / PS50923 / 2 / PS50948
9 / PS50835 / 2 / PS51092
10 / PS50026 / 2 / PS51115
24 / PS50231 / 2 / PS51117
52 / PS50041 / 3 / PS50227
3 / PS50240
3 / PS50261
3 / PS51127
3 / PS51132
4 / PS50221
4 / PS50927
7 / PS50228
8 / PS50963
10 / PS50923
12 / PS50835
13 / PS50026
17 / PS50231
61 / PS50041
Total-151 / Total-200
All reported domains reported in cancer and non-cancerlectins with their frequency of occurrence. A total of 151 and 200 PROSITE domains were reported in cancer and non-cancer lectins respectively.
Table S10: The performance of amino acid composition based SVM model (Learning Parameter: -z c –t 2 –g 0.01 –c 0.5 –j 1) using random dataset of cancer and non-cancerlectins.
Threshold / Sensitivity / Specificity / Accuracy / MCC-1.0 / 96.55 / 4.03 / 50.63 / 0.04
-0.9 / 95.06 / 7.03 / 51.37 / 0.06
-0.8 / 91.60 / 10.56 / 51.38 / 0.05
-0.7 / 87.15 / 13.06 / 50.39 / 0.02
-0.6 / 85.15 / 17.08 / 51.39 / 0.04
-0.5 / 81.68 / 21.60 / 51.89 / 0.04
-0.4 / 76.23 / 29.18 / 52.88 / 0.06
-0.3 / 76.23 / 29.18 / 52.88 / 0.06
-0.2 / 68.30 / 41.21 / 54.88 / 0.10
-0.1 / 61.40 / 46.24 / 53.89 / 0.08
0.0 / 57.93 / 50.77 / 54.38 / 0.09
0.1 / 49.46 / 56.82 / 53.12 / 0.07
0.2 / 44.99 / 61.35 / 53.12 / 0.06
0.3 / 40.04 / 68.37 / 54.11 / 0.09
0.4 / 34.61 / 73.92 / 54.12 / 0.10
0.5 / 23.71 / 76.42 / 49.87 / 0.00
0.6 / 17.76 / 83.45 / 50.36 / 0.01
0.7 / 11.83 / 87.47 / 49.37 / -0.01
0.8 / 7.88 / 90.50 / 48.88 / -0.02
0.9 / 6.90 / 93.00 / 49.62 / 0.00
1.0 / 3.95 / 96.50 / 49.87 / 0.01
(Bold value indicates the point where overall best result was achieved.)